AN ALGORITHM FOR SIMULATION AND STOCHASTIC SPATIAL OPTIMIZATION OF THE SAUSAGE PRE-SMOKING HOLDING PROCESS
DOI:
https://doi.org/10.26408/139.05Keywords:
production logistics, stochastic optimization, Monte Carlo method, simulation model, pre-smoking holding, meat processingAbstract
The objective of this study was to create a simulation-based model to optimize the logistics of industrial sausage pre-smoking holding. Due to the high computational complexity and the high number of coupled degrees of freedom, a modification of the Monte Carlo method was implemented. The simulation results demonstrated and quantified the influence of key spatial and temporal parameters describing the sausage pre-smoking holding process. The stochastic optimization algorithm provided a total delay reduction of 25–40% and an end delay reduction of up to 76% for different arbitrarily conceived, although varied, high-volume production plans. The results indicated that pre-smoking holding delays in lower-volume production could be difficult to minimize.
References
Abakarov, A., Sushkov, Y., Almonacid, S., & Simpson, R. (2009). Multiobjective optimization approach: thermal food processing. Journal of Food Science, 74(9), 471-487.
Adeyemi, K. D., & Abdulkadir, L. O. (2025). Roasting temperature and fat type influence cholesterol oxidation products, fatty acid composition, physicochemical properties and sensory attributes of beef sausages. PLoS One, 20(4), e0322290.
Aggarwal, D., Bennett, H., Brakerski, Z., Golovnev, A., Kumar, R., Li, Z., Peters, S., Stephens-Davidowitz, N., & Vaikuntanathan, V. (2022). Lattice problems beyond polynomial time. Proceedings of the 55th Annual ACM Symposium on Theory of Computing (pp. 1516-1526).
Akpinar, M. E. (2021). A logistic optimization for the vehicle routing problem through a case study in the food industry. Logforum, 17(3), 387-397.
Arnau Arboix, J. (2025). Sensory Defects in Fermented Sausages. 2025 Update, Friggomeccanica, Sala Baganza (PR).
Banjarnahor, F. S. (2024). Efficiency analysis of production process line sausage processing with queuing theory approach (case study at PT.XYZ) (Doctoral dissertation). Universitas Bakrie.
Bykowski, P., & Wilczyńska, A. (2010). Mięso zwierząt rzeźnych i przetwory. In P. Przybyłowski (Ed.), Towaroznawstwo artykułów spożywczych. Część I (pp. 139-161). Wydawnictwo Akademii Morskiej w Gdyni.
Fernandes, I. D. A. A., Pedro, A. C., Ribeiro, V. R., Bortolini, D. G., Ozaki, M. S. C., Maciel, G. M., & Haminiuk, C. W. I. (2020). Bacterial cellulose: From production optimization to new applications. International Journal of Biological Macromolecules, 164, 2598-2611.
Gailan Qasem, A., Aqlan, F., Shamsan, A., & Alhendi, M. (2021). A simulation-optimisation approach for production control strategies in perishable food supply chains. Journal of Simulation, 17(2), 211-227.
Kabiesz, P., & Bartnicka, J. (2018). Ergonomic and workflow study of sausage production process in the context of manual transport tasks. Multidisciplinary Aspects of Production Engineering, 1(1), 695-701.
Katiyar, S., Khan, R., & Kumar, S. (2021). Artificial bee colony algorithm for fresh food distribution without quality loss by delivery route optimization. Journal of Food Quality, 2021(1), 4881289.
Kiriş, S. B., & Ünal, M. (2020). Evaluating the performance of the production line with simulation approach in meat processing industry: A case from Turkey. Alphanumeric Journal, 8(1), 1-16.
Koroteev, M., Romanova, E., Korovin, D., Shevtsov, V., Feklin, V., Nikitin, P., Makrushin, S., & Bublikov, K. V. (2022). Optimization of food industry production using the Monte Carlo simulation method: A case study of a meat processing plant. Informatics, 9(1), 5.
Korzeniowska-Ginter, R., & Keller, A. (2019). Ocena parametrów technologicznych produkcji kabanosów tradycyjnych. Postępy Techniki Przetwórstwa Spożywczego, 1/2019, 76-81.
Kroese, D. P., Brereton, T., Taimre, T., & Botev, Z. I. (2014). Why the Monte Carlo method is so important today. Wiley Interdisciplinary Reviews: Computational Statistics, 6(6), 386-392.
Kuo, J., Pan, K., Li, N., & Shen, H. (2020). Wind farm yaw optimization via random search algorithm. Energies, 13(4), 865.
Latoch M. (2024). Zarys technologii produkcji wędlin. In M. Michalak-Majewska (Ed.), Opracowanie podsumowujące projekt Rzemieślnicza produkcja żywności i kosmetyków w oparciu o naukę i praktykę (pp. 31-45). Wydawnictwo Uniwersytetu Przyrodniczego w Lublinie.
Lemma, Y., Kitaw, D., & Gatew, G. (2014). Loss in perishable food supply chain: An optimization approach literature review. International Journal of Scientific & Engineering Research, 5(5), 302-311.
Lisowski, J. (2017). Metody optymalizacji. Wydawnictwo Akademii Morskiej w Gdyni.
Lones, M. (2011). Sean Luke: Essentials of metaheuristics. Genet Program Evolvable Mach, 12, 333-334.
Migdał, W., Gubała, D., Kołdras, M., Wołek, D., & Migdał, Ł. (2023). Wykorzystanie mięsa rodzimych ras bydła do produkcji wędlin. In W. Chabuz, W. Sawicka-Zugaj (Ed.), Lokalne rasy zwierząt w ochronie bioróżnorodności i zachowaniu tradycji regionów (pp. 52-68). Wydawnictwo Uniwersytetu Przyrodniczego w Lublinie.
Migdał, W., Migdał, Ł., Walczycka, M., Węsierska, E., Zając, M., Tkaczewska, J., & Kulawik, P. (2017). Poprawa cech sensorycznych i trwałości mięsnych produktów regionalnych pozyskiwanych od rodzimych ras zwierząt poprzez wykorzystanie tradycyjnych metod wędzenia. Wiadomości Zootechniczne, 55(5), 168-176.
Nizio, E., Czwartkowski, K., & Niedbała, G. (2023). Impact of smoking technology on the quality of food products: Absorption of Polycyclic Aromatic Hydrocarbons (PAHs) by food products during smoking. Sustainability, 15(24), 16890.
Pacheco, J., Alvarez, A., García, I., & Angel-Bello, F. (2012). Optimizing vehicle routes in a bakery company allowing flexibility in delivery dates. Journal of the Operational Research Society, 63(5), 569-581.
Pratap, S., Jauhar, S. K., Paul, S. K., & Zhou, F. (2022). Stochastic optimization approach for green routing and planning in perishable food production. Journal of Cleaner Production, 333, 130063.
Redondo-Solano, M., Burson, D. E., & Thippareddi, H. (2016). Thermal resistance of Clostridium difficile spores in peptone water and pork meat. Journal of Food Protection, 79(9), 1468-1474.
Roy, L., Bera, D., & Garlapati, V. K. (2020). Evolutionary optimization techniques as effective tools for process modelling in food processing. In S. Sevda, S. Anoop (Eds.), Mathematical and statistical applications in food engineering (pp. 5-20). CRC Press.
Samúelsson, D. G. (2019). Process simulation of manufacturing SS Wiener Sausages (Doctoral dissertation), Reykjavík University.
Simpson, R., Abakarov, A., & Teixeira, A. (2008). Variable retort temperature optimization using adaptive random search techniques. Food Control, 19(11), 1023-1032.
Therdthai, N. (2021). Modeling and optimization of food processes. In S. M. Jafari (Ed.), Engineering principles of unit operations in food processing (pp. 419-441). Woodhead Publishing.
Tsai, C. W., & Chiang, M. C. (2023). Handbook of metaheuristic algorithms: from fundamental theories to advanced applications. Elsevier.
Wari, E., & Zhu, W. (2016). A survey on metaheuristics for optimization in food manufacturing industry. Applied Soft Computing, 46, 328-343.
Wawrzyniak, K., Ligaj, G., Wióra, B., Zielińska, S., & Baluta, S. (2023). Technologies in drying of meat products. Systemy Wspomagania w Inżynierii Produkcji, 12(2), 28-42.
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